The present invention relates generally to intelligent camera systems and more particularly to a system, method and program product that may employ camera systems in order to discover social networks.
Video-based site surveillance systems are currently employed in a variety of public, private, and semi-public settings including, schools, parks, shopping malls, neighborhoods, prisons and prison yards, and the like. Some advances have been made in camera systems to include, for example, facial recognition via images gathered from the automated camera systems. Typically though, imagery is gathered from the system only after a crime, or series of crimes, has occurred for analysis in order to attempt to aid in solving the crime or providing post-crime improvements (e.g., changing security personnel and/or equipment, etc.).
In order to attempt to improve predictive security efforts, law enforcement attempt to gain a high level understanding of crowd behavior in terms of interaction and social network patterns. A social network consists of groups of people with a pattern of interactions between them and the understanding of such social networks in various environments, such as prisons or public venues is of great interest to law enforcement and homeland security. There is an increasing need to identify cohesive groups and their leaders for security purposes. It is thought that being able to identify, for example gangs and their leaders, including any changes to those structures over time, would be of great value to the security industry. Ultimately, any improvement in identifying these various social structures before the crime(s) is committed can only aid security and law enforcement efforts in their efforts.
Heretofore, these identification efforts have typically been limited to personnel manually observing social relationships in areas either in real-time by actual observation (e.g., prison guard watching prison yard), watching camera feeds on video screens (e.g., security guard in video command center), and/or personnel reviewing video data collected after the fact. All of these methods are time consuming and highly inefficient.
Accordingly, there is an ongoing need for further improving the “intelligence” of video-based site surveillance systems.